Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers

The deployment of neural networks on resource-constrained microcontrollers has gained momentum, driving many advancements in Tiny Neural Networks. This paper introduces a tiny feed-forward neural network, TinyFC, integrated into the Field-Oriented Control (FOC) of Permanent Magnet Synchronous Motors (PMSMs). Proportional-Integral (PI) controllers are widely used in FOC for their simplicity, although their limitations in handling nonlinear dynamics hinder precision. To address this issue, a lightweight 1,400 parameters TinyFC was devised to enhance the FOC performance while fitting into the computational and memory constraints of Figure 1: Workflow diagram to deploy NN-augmented FOC a micro-controller. Advanced optimization techniques, including pruning, hyperparameter tuning, and quantization to 8-bit integers, such as automotive, industrial, naval and aeronautics, where compact were applied to reduce the model's footprint while preserving the size and precision control are essential [19]. PMSMs consist of network effectiveness. Simulation results show the proposed approach a stator housing the windings and a rotor containing permanent significantly reduced overshoot by up to 87.5%, with the magnets. The operational interaction between the stator's rotating pruned model achieving complete overshoot elimination, highlighting magnetic field and the rotor's fixed magnetic field enables synchronization the potential of tiny neural networks in real-time motor control at synchronous speed [10].